epiGWAS: Robust Methods for Epistasis Detection

Functions to perform robust epistasis detection in genome-wide association studies, as described in Slim et al. (2018) <doi:10.1101/442749>. The implemented methods identify pairwise interactions between a particular target variant and the rest of the genotype, using a propensity score approach. The propensity score models the linkage disequilibrium between the target and the rest of the genotype. All methods are penalized regression approaches, which differently incorporate the propensity score to only recover the synergistic effects between the target and the genotype.

Version: 1.0.0
Depends: R (≥ 3.4.0)
Imports: matrixStats, DescTools, glmnet, SNPknock
Suggests: foreach, iterators, precrec, parallel, doParallel, bigmemory, biglasso, testthat, knitr, rmarkdown, kableExtra
Published: 2018-12-16
Author: Lotfi Slim [aut, cre], Clément Chatelain [ctb], Chloé-Agathe Azencott [ctb], Jean-Philippe Vert [ctb]
Maintainer: Lotfi Slim <lotfi.slim at mines-paristech.fr>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: README NEWS
CRAN checks: epiGWAS results


Reference manual: epiGWAS.pdf
Vignettes: Robust epistasis detection with epiGWAS
Package source: epiGWAS_1.0.0.tar.gz
Windows binaries: r-devel: epiGWAS_1.0.0.zip, r-release: epiGWAS_1.0.0.zip, r-oldrel: epiGWAS_1.0.0.zip
OS X binaries: r-release: epiGWAS_1.0.0.tgz, r-oldrel: epiGWAS_1.0.0.tgz


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